Skip to main content
Glama

opentable_make_reservation

Book a restaurant reservation on OpenTable by specifying restaurant ID, date, time, and party size. Use confirm to preview booking details before finalizing.

Instructions

Book a restaurant reservation on OpenTable. Set confirm=false to preview before booking, confirm=true to actually book. Requires the user to be logged in.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesReservation date in YYYY-MM-DD format
timeYesReservation time in HH:MM format (e.g. '19:00')
confirmYesSet to true to actually book the reservation, false to just preview details
partySizeYesNumber of guests
restaurantIdYesThe restaurant ID or profile URL
specialRequestsNoAny special requests or dietary requirements (optional)
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure, and it delivers by revealing that the user must be logged in and by explaining the preview-vs-commit workflow. However, it doesn't discuss potential side effects like double-booking or idempotency of repeated calls, though the confirm mechanism mitigates concern.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, with each one serving a distinct purpose: stating the core action, explaining the critical delete-style safety mechanism, and flagging a prerequisite. Every word earns its place, and no information is repeated or redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 6 parameters and no output schema, the description covers the key operational aspects: the preview/booking flow and authentication requirement. The main gap is silence on what the tool returns after a successful booking (e.g., confirmation details), which is left undefined since no output schema exists.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description reiterates the confirm parameter logic, which reinforces its importance but adds nothing new beyond the existing schema descriptions. It confirms the confirm-flow but doesn't inform on specialRequests, date, or other params in a way that goes beyond the schema's documented format requirements.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb+resource construction ('Book a restaurant reservation on OpenTable') and the additional context about confirm=true/false clarifies the dual preview/booking behavior. This clearly differentiates it from siblings like check_availability, get_reservations, and cancel_reservation, which serve distinct purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies a usage pattern ('Set confirm=false to preview before booking') but never explicitly states when to use this tool versus alternatives like opentable_check_availability or opentable_get_reservations. The workflow guidance is implied through the confirm parameter rather than stated as explicit tool-selection criteria, which puts it at the 'implied usage' baseline.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/markswendsen-code/mcp-opentable'

If you have feedback or need assistance with the MCP directory API, please join our Discord server